(SpringerBriefs in Business Process Management) Learning Analytics Cookbook_ How to Support Learning Processes Through Data Analytics and Visualizatio

48 5 Learning Analytics in a Primary School: Lea’s Box Recipe
Fig. 5.2 Example of a student’s competence space
competencies C17 and C18. Thus, you can identify subordinate and superordinate
groups, relationships, and learning paths.
A similar visualization is “Competence Spaces” (Fig. 5.2), which shows the
nonlinear structure of a course’s competencies. The bottom of the diagram depicts
a course’s (or learning domain’s) starting point, where the learners hold none of the
competencies. The colors indicate the probability that a student has the competencies,
where a darker color indicates higher probability. The lines show possible
learning steps from one state to another—that is, the possible learning paths through
the course. The example in Fig. 5.2 shows the concrete learning paths of a specific
students (thicker red line) and the learning state with the highest probability (dark
blue). The magnified area shows the competencies (C01–C08) this student has, as
well as the optimal next learning steps (o1–o3).
In Fig. 5.2, the easiest competencies (or skills) are at the bottom and the most
difficult are on top. The lines indicate possible learning paths to take from a state of
having none of the competencies to a state of having all of them. This type of
visualization also allows the next optimal learning steps for each individual to be
identified, and it forecasts whether a specific student is at risk of failing the course
because he or she is unlikely to complete the course goals in the remaining time.
Another visualization is the so-called “Learning Landscape,” as shown in
Fig. 5.3. The screen shows the students, their assignments, and their achievements
in a landscape-like way. For example, Fig. 5.3’s left panel shows a number of the
course’s exercises clustered based on their difficulty (or, in other words, the